A self-powered and self-sensing human kinetic energy harvesting system for application in wireless smart headphones

Abstract

With the rapid development of wireless communication technology, intelligent wearable devices with various functions are increasingly appearing in our daily lives. However, these wearable devices, such as wireless headphones, typically have shorter battery life and require long-time charging from an external power source, seriously affecting the user experience. In order to enhance the battery life and the intelligence of wireless headphones, this paper proposes a human kinetic energy harvester based on electromagnetic-triboelectric hybrid power generation mechanism. The kinetic energy harvester is embedded inside the earphone, which can effectively collect low-frequency motion energy of the human body and use it to charge the wireless headphone. On the other hand, based on this kinetic energy harvester, intelligent control of headphones can be achieved through head movement. In terms of energy harvesting performance, experimental results show that under vibration excitation of 4 Hz-20 mm, the maximum power obtained by triboelectric nanogenerator unit (TENG) is 4.09 μW, corresponding to a power density of 0.15 μW cm⁻³ and a matching resistance of 154 MΩ. For electromagnetic power generation unit (EMG), under the same excitation conditions, the maximum output power is 67.19 μW, corresponding to a power density of 2.38 μW cm⁻³ and a matching resistance of 40 Ω. In addition, the proposed hybrid kinetic energy harvester can be coordinated with machine learning algorithms to analyze and recognize the electrical signals obtained by the kinetic energy harvester, thereby achieving intelligent control of headphone working modes through head movements. The experimental results show that, based on the Long Short Term Memory (LSTM) model, the energy harvester can achieve a recognition accuracy of 99.53 % in recognizing the two states of head nodding and head shaking. This work not only enriches the application scenarios of energy harvesting technology, but also provides a new solution for wireless headphones to achieve self-power and intelligent human-machine interaction.